Okay. This book and I didn't get along terribly well, but the experience was nevertheless a valuable one. So, 3 stars, even though I disagree fundamentally with some of the theory and the style of presentation. This will be a long one; bear with me.To put it simply.... Jeff Hawkins is a very intelligent computer engineer who thinks he understands brains in ways that no neuroscientist ever has before, mostly because he is willing to stand by a grand picture where most neuroscientists want to investigate every small chunk before declaring they've solved brains. He has read a lot of books about neuroscience and has spoken to a lot of neuroscientists, and has trudged up a (not patently incorrect) theory from the 1970's and used it as a foundation for what he considers the first general theory of cortical brain functioning (it isn't). He then equates "cortex" with "intelligence" and takes off on a grand tour of his theory: that we can build intelligent* machines to perform complex pattern recognition tasks in much the same ways that he proposes an organic cortex does.*NOT human-like, though you wouldn't know it from the wording in any blurb you read about the book, including the book's own jacket summary.There are a lot of theoretic assumptions in this book, and unpacking them is quite unfortunately left up to the reader, who may not have the requisite background knowledge to separate out responsible assumptions, backed by data that Hawkins rarely mentions in order to keep it digestible to a lay audience, from irresponsible ones. There are many of the latter, detectable only by those who know the field or the scientific method well enough to know a red flag when they see it. Hawkins plays loose on owning up to these assumptions, even when they are cornerstones without which his theory loses a lot of its appeal. I was relieved when he admitted to the oversimplification of his view at the end of the chapter on neurology, but it felt like an afterthought that could (should?) have been used to temper his conviction and factual flippancy up to that point. The tone of the book is occasionally that of a conspiracy theorist who has figured it all out, against all conventional belief, and pulls you along a fast sequence of premises and conclusions while waving his hands and telling you the details of the premises are too complicated to get into.One of the bafflers that stood out to me (this is a bit technical, sorry) was the notion that the basal ganglia and cerebellum are old structures whose functions have been largely subsumed by the neocortex, and thus were unnecessary for a theory of intelligent motor behaviour. The balls it takes to make that claim, when such vastly debilitating diseases as Parkinson's, Huntingtons, or Ataxia exist, blew my brains out a bit.At that point, it became clear that Hawkins was so fixated on the neocortex that he was willing to push aside contradictory evidence from subcortical structures to make his theory fit. I've seen this before, from neuroscientists who fall in love with a given brain region and begin seeing it as the root of all behaviour, increasingly neglecting the quite patent reality of an immensely distributed system. It's pretty natural, and honestly not limited to neuroscientists: when you stare too closely at one piece of a puzzle, you begin to forget that there are other pieces. For most scientists, however, this view need not be a detriment, because they generally aim their research programs at very specific questions-- questions that this atomized focus are fit to answer. In the case of Jeff Hawkins' general theory of brain function, however, it's entirely disingenuous.Putting aside my qualms with his approach to the theory, there is actually some overlap between his views my own, and some points of valuable and probably instructive disagreement. Hawkins views intelligence as a result of hierarchical and recursive neural organization: basically, there are higher and lower levels, with communication tracing both upwards (from sensory input) and downwards (from higher levels of analysis) via patterns of activation. What we experience is a complex interaction between external input and internal input from memory, resulting in a continuous stream of online prediction. Up to this point, my theories of what we might call consciousness match his theory of intelligent pretty well. Where we differ is in the details (and in our respective convictions that we are correct!).In neuroscience, there is a theoretical construct called the 'grandmother cell,' to illustrate the ludicrous idea that there is a single neuron in your brain that represents your grandmother, another for your cat, and so on. The grandmother cell has been disproven time and time again: the brain is a HIGHLY distributed system, and a given representation is the result of patterns of activations across many cells, not one cell. Jeff Hawkins acknowledges this.... before proposing instead that representations in the cortex are handled by (my term, not his) grandmother cellular columns. Briefly, the visual cortex has been shown to have a columnar organization that traverses six parallel layers of anatomically and biologically different cell types, such that cells at Point X of layer 1 respond to the same sorts of basic visual information (e.g., line angles) as cells at Point X of layers 2 through 6. Because the rest of the cortex also seems to be organized in six distinguishable layers, Hawkins suggests that the entire cortex operates in columns, such that the composite idea of your grandmother should be represented by a given cellular column in a high-level area of cortex. He never states these logical conclusions outright, but they follow from the way his theory proposes hierarchical organization to work. He briefly admits the oversimplification at play, and then nevertheless uses the oversimplification as the foundation for the rest of the theory. This is not a novel theory so much as it is an outdated theory with the goalpost pushed back one step. And while oversimplifications are a necessary evil in scientific progress*, they need to be acknowledged and admitted so that they can be refined, again, especially where a general theory for a lay audience is the goal.*as my advisor says it, the goal of a scientist is to maintain a productive level of ambiguity.The rest of the book was (to me) less controversial. There was the requisite chapter to answer such questions as "does this mean animals are intelligent!?" for readers who've never thought about the implications of a physical and evolutionarily-derived brain before. Yawn. This was followed by a chapter on what I assume is the whole reason Hawkins wrote the book: the prospect of intelligent machines. Having defined 'intelligence' as 'cortex,' he rather plainly announces that an intelligent machine will be one organized with recursive and flexible hierarchies, a reveal that will shock or excite no cognitive scientist. He very clearly explains why current artificial intelligence built on existing computer memory structures are not up to the task, an argument that AI researchers have been ignoring for decades. Much to my pleasant surprise, again given the blurbs on this book, he then laments the cold hard reality that we will never have viable machines that are intelligent in the way humans are: humans are intelligent the way humans are because of all the sensory and proprioceptive input coming in from their human bodies to shape their brains. Unless we build almost impossibly costly and cumbersome human-like bodies to go with our fancy intelligent machine brains, it's a moot point trying to make machines like humans-- and why would we want to anyways? Hawkins outlines some realistic goals that are achievable (e.g., self-driving cars, diagnostic machines, weather prediction machines...), none of which I particularly disagree with except for the optimistic time-frames forecasted. However, I can't help feeling that most readers are set up to be vastly disappointed by the propositions. With the majority of the book devoted to neurological theory, it's hard not to anticipate that the machine intelligence he will eventually propose will mimic neurology. The book jacket itself claims that Hawkins' theories will "make it possible for us to build intelligent machines, in silicon, that will not simply imitate but exceed our human ability in surprising ways." But nothing about the analogical applications of his neurologically-based theory are intended to imitate humans. He expressly states, in fact, that the aim to build artificial humans is wrong-footed and fated to fail. The message is a bit confusing, and while I would have personally been offended to hear him say what most readers likely wanted to hear-- that we can build human-like machines by analogy to human cortex-- I again get a sticky sense of disingenuity, this time to sell book copies.Overall, this was an interesting but infuriating book that takes some great ideas from existing cognitive science, laudably exposes them to a lay audience in ways that most cognitive and neuroscientists won't bother to, shoves them into a flawed neurological framework, and then announces brains to be solved. The ego involved is staggering, the conclusions less so, and the applications underwhelming. I am admittedly very interested to see, hopefully in my lifetime, just how intelligent Hawkins' intelligent machines can get with only an analogical neocortex. Since he never discusses this fact, spoilers: a neocortex is not enough for either humans or nonhuman animals to function, let alone intelligently. The (rather expensive...) exercise of trying to evoke intelligence from cortex alone could provide us with a better appreciation of subcortical structures, much needed in this species-self-congratulatory era of cortical fixation.

I can't really say this was a practical book but it definitely gives a different perspective on how the brain works and how the current AI implementations are totally off the target. It's enlightening. Worth the read if you are a software developer for sure.

Jeff Hawkins is most commonly known for inventing one of the first handheld computer devices, the palm pilot, and founding the Redwood Center for Theoretical Neuroscience. Although he has expressed interest in artificial intelligence his whole life, he has also expressed a deep interest for Neuroscience as shown in his book On Intelligence. In this book he brings the ideas of artificial intelligence and neuroscience together to present his theory of how the brain processes information. Here are my thoughts on the book:The first point that Hawkins makes is that in order to create truly intelligent machines, we need to understand how the brain functions, and apply that to artificial intelligence. Although neuroscientists will never be able to fully understand how the brain functions, a general understanding of the brain will be enough to apply on artificial intelligence. He then presents the reader with his theory of how the cortex of the brain works. His theory is that the brain is essentially a series of hierarchical patterns that perceive information through the different senses. He claims that the senses all work together to make predictions, and therefore create patterns that we dont even know. This part of the book (chapter 6) is very detailed and at times hard to understand because its all about Hawkinss theory of how the cortex works. Hawkins builds on all of his ideas throughout the book, making each of them important to fully understand in order to understand the next section. A lot of the book is this written this way, where he explains very difficult specific details in a very general way. For example he says, Second, a face can appear on the left side of your V1 or on the right side of your V1 and you would recognize it. But experiments clearly show that non adjacent patches of V1 are not directly connected; the left side of V1 cant know directly what the right side is seeing (Hawkins 121). Although this idea is pretty clear by itself, he continues to build on it until on its very hard to understand. He tries to put a lot of difficult ideas into a short amount of space, making it impossible to clearly explain every detail. Because he goes so in depth it takes a lot of thought to process the information. Although he doesnt explain everything with a lot of detail, he does use a lot of effective analogies. For example, he uses the analogy of breaking a pen to show how the brain predicts from experience. If you broke a pen you would expect to see it break in half, and hear it snap. If you didnt see/hear both of these things then your brain would know that something was wrong. These parts of the book are easier to understand and are quite interesting. Overall, some parts of the book were harder for me to understand because of my unfamiliarity with neuroscience, while other parts were more straightforward.Another point that Hawkins brings up is that intelligence is defined as consciously processing information, and using predictions to create patterns. Therefore, he concludes that humans animals, and essentially all living things are intelligent to a degree. Trees are intelligent in the sense that they pick up signals from their environment and send them to their roots, branches, and leaves to alter or change its state. The tree is making patterns, but is doing it automatically without knowingly processing it and is therefore not as intelligent as a human. What distinguishes plant intelligence from human intelligence is the idea that humans can interpret and process the different things we encounter. However, I find myself disagreeing with Hawkins definition of intelligence. Personally I think that intelligence is also defined by interpreting patterns and making predictions, but I think intelligence can be automatic like a plant. To me a plant is just as intelligent as a human because they both have to interpret their surroundings to alter their situation and make prediction based patterns. I understand the point Hawkins makes and his definition of intelligence, but I dont think a plant is any less intelligent than a human. While reading the book, Hawkins also uses a lot of ideas and theories from other neuroscientists. However, most of the theories he disregards as false to make it seem like he is the only comprehensive neuroscientist. He is constantly stating why every other theory about the brain is wrong, and why he is correct. I understand that in order to create a theory you have to go against what others have said, but neuroscience is such an unknown topic that one cannot completely disregard all other theories. He is very credible and I dont doubt that he completely understands what he is talking about; however, I wish he wouldnt have disregarded so many other theories about neuroscience. Aside from the sometimes difficult language of the book, the book overall is very interesting and a good read. He does use a lot of analogies that are helpful, and has corresponding images throughout the book that help visualize what he is talking about. In the beginning he is very repetitive with his purpose, clearly explaining his question of how to join neuroscience with artificial intelligence. Its very interesting to learn about how humans think, and learn how we make predictions that we dont even realize. Its exciting to relate to the book and connect what hes saying to how we think. He finally concludes with the idea that building intelligent machines could possibly dangerous, but could benefit society greatly. We could create intelligent voice recognition devices (like siri) that could understand and interpret language. We could create machines that process information like the brain, but faster, with more capacity, and with sensory detections. Creating truly intelligent machines would completely change the outline of society for the better. Overall, I would recommend this book to anyone with a genuine interest in neuroscience and/or artificial intelligence. It is at times hard to understand but very interesting and mind-opening. Hawkins explores all aspects of human, plant, and artificial intelligence and brings it all together in one idea.

Although published in 2004, this book still gives many insights on how information flows through the brain and how perception arises. Nowadays, when scientific news are full of breakthroughs achieved by AI and machine learning algorithms, that`s exactly the moment to ask - how far this algorithms from the actual work of the brain? Does it need to imitate brain at all in order to achieve the same level of object recognition and sophistication as humans have? According to the book, the main feature of the brain - it is not the complex behavior, imagination or learning capabilities, but memory and ability to make predictions consistent with the memory, and then, change behavior if needed. Many species have quite developed memory, and also do some kind of predictions - no doubts, it`s beneficial from evolutionary perspective, but, in addition to the huge volume of this memory, humans have completely different type of connections in the brain. Motor system is under the control of cortex, unlike the animals, where movements are controlled by the older parts of the brain. Unique layered structure of the cerebral cortex allows immediately detect all changes across the visual field and keep the principal object of perception stable at the same time. Such representation called invariant and provided by the hierarchical structure of cortex, which is true for each region of cortex - visual, auditory or motor. Authors also points out how feedback on each level is important as it sends back information about if prediction was correct or not. While many neuroscientists study something deeply focused and narrowed about the brain, the problem of consciousness and intelligent will not be clarified without attempts to summarize everything we know about how the cortex works. It doesn`t make sense to study how vision or sensory perception works separately, but essentially to figure out what is in common between them while processing through the cortex. So the biggest challenge of intelligence is to unravel the intelligence by itself.

Jeff Hawkins has done a remarkable thing. He's essentially synthesised all of the information we have on how the brain works into a simple, elegant and utterly comprehensible theory of intelligence that will pave the way to the creation of truly intelligent machines. That's a massive claim I know but I honestly don't think I have ever read a simpler, more straightforward account of what intelligence is. Hawkins' theory, in a nutshell, is that intelligence is a manifestation of the brains ability to predict the future and test its perceptions against its predictions. Like a fractal there is a mass of self-similarity at work here. At the very fine-grained level the predictions the brains making are very mundane but as sensory information is handled, and exceptions passed up the hierarchy, and predictions passed back down the hierarchy, our brains learn from their experiences and, over time a genuine, common understanding of the world emerges. Anyone working on machine intelligence should read this short, simple book.